DataRobot-Research/drdocs huggingface dataset:
---
title: Trial FAQ
description: Questions and answers about DataRobot's self-service trial experience.

---

# Trial FAQ {: #trial-faq }


??? faq "What is self-service SaaS?"
	DataRobot's _self-service_ SaaS includes the same capabilities and features that are available in the managed AI Platform enterprise software. It provides your organization with the opportunity to explore enhanced capabilities and fully test functionality in AI/ML projects. Through _self-service_ SaaS, users can sign up, manage their organization and users, and run workloads&mdash;all without needing to speak to a DataRobot representative.

??? faq "What is DataRobot’s self-service SaaS trial?"
	DataRobot’s Self-Service SaaS trial, or “Trial,” is a one-time, 30-day free trial period for organizations to fully explore the platform’s features without any financial commitment. The 30-day trial is based on 30 calendar days, which begins the day you register.  

??? faq "Who can access DataRobot’s Self-Service SaaS Trial?"
	Trial is available to anyone who would like to try DataRobot. Sign up with an email and password, or with a social login such as Google or GitHub.

??? faq "Can Trial users collaborate with teammates?"
	Yes. You can invite colleagues to join your trial account. Once the colleagues have accepted the invite, they become part of your DataRobot _organization_. You can then share Use Cases, datasets, and other assets through all of the traditional sharing methods or by adding the colleagues to a Use Case. Additionally, you can create [No-Code AI Apps](wb-apps/index){ target=_blank } and share the app with users both within and outside of DataRobot through an app-specific sharing link.

??? faq "Can Trial users share with teammates outside their organization?"
	Trial users can only share assets within their DataRobot organization, not across organizations. If a user joins under a different organization, even if they are teammates from the same company, they cannot share datasets or assets in a Use Case, although they can still share No-Code AI Apps.  


??? faq "How do trial users get product support?"
	Trial users do not have access to DataRobot Customer Support teams, or the Customer Support ticketing system. For product support, users can post questions in the [DataRobot Community]((https://community.datarobot.com){ target=_blank }.

??? faq "Does trial offer full DataRobot functionality?"
	Yes, you will have access to the full power of the platform with both Workbench and DataRobot Classic interfaces. Each account is assigned a specific number of modeling workers, and those workers are shared by all users in the account. Trial users are provided access to batch predictions only.

??? faq "What if I want to continue after the 30 days have passed?"
	After the 30-day trial has ended, you can reach out to Sales to upgrade your account.
# Amazon Redshift {: #amazon-redshift }

## Supported authentication {: #supported-authentication }

- Username/password

## Prerequisites {: #prerequisites }

The following is required before connecting to Redshift in DataRobot:

- Amazon Redshift account

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with Redshift:

Required field | Description |  Documentation
--------------- | ---------- |  -----------
`address`       | The connection URL that supplies connection information for Redshift.<br><br>**Example:** `redshift.cjzu88438s1ja.us-east-1.redshift.amazonaws.com:7632`  |   [Redshift documentation](https://docs.aws.amazon.com/redshift/latest/mgmt/jdbc20-build-connection-url.html){ target=_blank }
`database`  | A unique identifier for your database.  | [Redshift documentation](https://docs.aws.amazon.com/redshift/latest/dg/r_CURRENT_DATABASE.html){ target=_blank }

Learn about additional [configuration options for Redshift](https://docs.aws.amazon.com/redshift/latest/mgmt/jdbc20-configuration-options.html){ target=_blank }.

{% include 'includes/data-conn-trouble.md' %}
---
title: Manage the cluster
description: Summarizes the admin tasks for managing a deployed cluster, such as connecting to external systems, and monitoring resources, activity, access, and licenses.
platform: self-managed-only

---

# Manage the cluster {: #manage-the-cluster }

!!! info "Availability information"
    Only Self-Managed AI Platform admins can manage their cluster.

To manage the deployed cluster, you can perform the following activities.

| Topic | Description... |
|-------------------------|-------------|
| [Configure external systems](sys-config) | Use the configuration management interface to ensure compatible configuration for LDAP and SMTP. |
| [Manage JDBC drivers](manage-drivers) | Add, delete, and control JDBC access. |
| [Manage access](manage-access) | Manage access to the cluster, including licenses, custom user agreements, and configuring SAML SSO. |
| [Monitor resources](resource-monitor) | Monitor resources in a standalone (Docker). |
| [Delete/restore projects](delete-restore) | Learn to delete and restore (or recover) projects. |
| [Apply a DataRobot license](license) | Apply a DataRobot license for a user via the UI or the API. |
---
title: Google
description: Deploying and monitoring DataRobot models on Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE).

---

# Google {: #google }

The following sections describe techniques for integrating the Google Cloud Engine with DataRobot:

Topic | Describes...
----- | ------
[Deploy and monitor models on GCP](google-cloud-platform) | Deploying and monitoring DataRobot models on the Google Cloud Platform (GCP).
[Deploy the MLOps agent on GKE](mlops-agent-with-gke) | Deploying the MLOps agent on Google Kubernetes Engine (GKE) to monitor models.
---
title: Analyze frequent values
dataset_name: N/A
description: How to use the Frequent Values chart, a histogram that shows the number of rows containing each value of a feature.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/tutorials/prep-learning-data/analyze-frequent-values.html

---

# Analyze frequent values {: #analyze-frequent-values }

In this tutorial, you'll learn how to use the Frequent Values chart, a histogram that shows the number of rows containing each value of a feature. You can also see the percentage of rows where the target is a particular value.

## Takeaways {: #takeaways }

This tutorial shows how to:

* Access and analyze the Frequent Values chart
* View average target values

## Analyze frequent values {: #analyze-frequent-values_1 }

Use the Frequent Values chart to compare the values of a feature.

1. [Import your dataset](import-data/index).

    ![](images/tu-eda-import-dataset.png)

    The sample dataset featured in this tutorial contains patient data.

    ![](images/tu-data-dataset.png)

    The goal is to predict the likelihood of patient readmission to the hospital. The target feature is `readmitted`.

    ??? tip
        See the [Assess data quality during EDA](assess-data-quality-eda) tutorial to learn how to use the Data Quality Assessment tool.

2. In the **Project Data** list, click a feature.

     For some features like categorical and boolean features, the **Frequent Values** tab is the default. For numeric features, the **Frequent Values** tab is to the right of the **Histogram** tab.

     The Feature Values chart displays each value that appears in the dataset for the feature and the number of rows with that value:

     ![](images/tu-frequent-values.png)

      For the `admission_type_id` feature, the most common values are *Emergency* and *Urgent*.

## View average target values {: #view-average-target-values }

After you kick off EDA2, you can also view the average target values for features.

1. Under **What would you like to predict**, enter your target feature.


2. Click **Start**.

     As soon as DataRobot finishes analyzing features, you can view the average target values in the Frequent Values chart.

3. In the **Project Data** list, select the feature you are analyzing.

    ![](images/tu-frequent-values-after-eda2.png)

    Notice the orange circles that overlay the histogram. The circles indicate the average target value for a bin.


## Learn more {: #learn-more }

**Related tutorials**

* [Assess data quality during EDA](assess-data-quality-eda)

**Documentation:**

* [Frequent Values chart](histogram#frequent-values-chart)
****
---
title: Datasets
description: The Datasets tab lists each dataset that has been added to your Use Case and provides the ability interact with those datasets, by exploring features, wrangling data, or creating an experiment.

---

# Datasets {: #datasets }

The **Datasets** tab lists all datasets currently linked to the selected Use Case by you and other team members. To access this tab, open a Use Case and click **Datasets**.

From this tab, you can:

![](images/wb-data-tab-5.png)

|          | Element   | Description |
|----------|-----------|-------------|
| <div class="table-label">1</div> | Add new      | Add a dataset, experiment, or notebook to your Use Case, or create a new Use Case. |
| <div class="table-label">2</div> | Search       | Search for a specific dataset. |
| <div class="table-label">3</div> | Sort         | Sort the dataset columns. |
| <div class="table-label">4</div> | More options | Click **More options** to interact with a dataset:<br><ul><li>**Explore**: View [Exploratory Data Insights](#view-exploratory-data-insights).</li><li>**Wrangle/Continue Wrangling**: [Perform data wrangling](wb-wrangle-data/index) on datasets retrieved from a data connection.</li><li>**Start modeling**: Set up an experiment using the dataset.</li><li>**Remove from Use Case**: Removes the dataset from the Use Case, also removing access for any team members. The dataset is still available via the [Data Registry](wb-data-registry).</li></ul> |

## View Exploratory Data Insights {: #view-exploratory-data-insights }

While a dataset is being registered in Workbench, DataRobot also performs [EDA1](eda-explained#eda1){ target=_blank }&mdash;analyzing and profiling every feature to detect feature types, automatically transform date-type features, and assess feature quality. Once registration is complete, you can explore the information uncovered while computing EDA1.

To view Exploratory Data Insights:

1. In a Use Case, navigate to the **Datasets** tab.
2. Click the **More options** icon next to the dataset you want to view and select **Explore**. Alternatively, click the dataset name to view its insights.

    ![](images/wb-data-tab-1.png)

3. For each feature in the dataset, DataRobot displays various [feature details](histogram){ target=_blank }, including a [histogram](histogram#histogram-chart){ target=_blank } and summary statistics.

    ![](images/wb-data-tab-2.png)

4. To drill down into a specific feature, click its histogram chart along the top.

    ![](images/wb-data-tab-3.png)

## Next steps {: #next-steps }

From here, you can:

- [Add more data.](wb-add-data/index)
- [Perform data wrangling for datasets added via a data connection.](wb-wrangle-data/wb-add-operation)
- [Use the dataset to set up an experiment and start modeling.](wb-experiment/index)
---
title: Image Augmentation
description: Describes the settings available from the Image Augmentation advanced option tab, where you can create new training images by randomly transforming existing images, thereby increasing the size of the training data.

---

# Image Augmentation {: #image-augmentation }


Train-time image augmentation creates new training images by randomly transforming existing images, thereby increasing the size of (i.e., “augmenting”) the training data. This allows projects to be built with datasets that might otherwise be too small. In addition, all image projects that use augmentation have the potential for smaller overall loss by improving the generalization of models on unseen data.

{% include 'includes/image-augmentation-include.md' %}

## Set transformations prior to modeling {: #set-transformations-prior-to-modeling }

After selecting your target, toggle on the **Image Augmentation** tab in **Advanced options**.

![](images/vai-ttia-ao-on.png)

From there, begin selecting transformation settings, described [here](ttia-lists#augmentation-lists). These settings will be applied to all models when running Autopilot or using the Repository.

![](images/vai-ttia-advanced-options.png)

You can continue to modify settings, clicking **Preview augmentation** to view a sample of results:

![](images/vai-ttia-advanced-options-1.png)


The settings you choose are automatically saved as a list named **Initial Augmentation List**. If you do not set transformations through **Advanced options**, you can later create augmentation lists using the [**Advanced Tuning**](adv-tuning) tab.
---
title: Batch prediction methods
description: Learn about DataRobot's available methods for scoring large files efficiently.
---

# Batch prediction methods {: #batch-prediction-methods }

DataRobot offers a variety of methods to efficiently score large files via batch predictions:

Method | Description
------ | ------------
[Batch Prediction UI](batch-dep/index) | Configure Batch Prediction jobs directly from the DataRobot interface included in deployments via MLOps.
[Batch Prediction API](../../api/reference/batch-prediction-api/index) | Use the API to create Batch Prediction jobs that score to and from local files, S3 buckets, the **AI Catalog**, and databases.
[Batch prediction scripts](cli-scripts) | Use command-line tools that wrap the Batch Prediction API, available for Windows, macOS, and Linux.

In addition, you can monitor and manage predictions using monitoring jobs and batch prediction jobs:

Method | Description
------ | ------------
[Monitoring Jobs UI](api-monitoring-jobs)  | Use the job definition UI to create monitoring jobs, allowing DataRobot to monitor deployments running and storing feature data and predictions outside of DataRobot.
[Monitoring Jobs API](ui-monitoring-jobs)  | Use the Batch Monitoring API to create monitoring job definitions, allowing DataRobot to monitor deployments running and storing feature data and predictions outside of DataRobot.
[Batch Jobs tab ](batch-jobs)              | Use the Batch Jobs tab to view and manage monitoring and prediction jobs.
??? note "Dataset requirements for time series batch predictions"
    To ensure DataRobot can process your time series data, configure the dataset to meet the following requirements:

    * Sort prediction rows by their timestamps, with the earliest row first.
    * For multiseries, sort prediction rows by series ID and then by timestamp.
    * There is *no limit* on the number of series DataRobot supports. The only limit is the job timeout, as mentioned in [Limits](batch-prediction-api/index#limits).

    For dataset examples, see the [requirements for the scoring dataset](batch-pred-ts#requirements-for-the-scoring-dataset).
---
title: Manage prediction job definitions
description: 
---

# Manage prediction job definitions

To view and manage monitoring job definitions, select a deployment on the **Deployments** tab and navigate to the **Job Definitions > Prediction Jobs** tab. 

![](images/batch-pred-job-def-list.png)

Click the action menu for a job definition and select one of the actions described below:

| Element | Description |
|---|---|
| View job history | Displays the **Deployments > Batch Jobs** tab listing all prediction jobs generated from the job definition. |
| Run now | Runs the job definition immediately. Go to the **Deployments > Batch Jobs** tab to view progress. |
| Edit definition| Displays the job definition so that you can update and save it. | 
| Disable definition| Suspends a job definition. Any scheduled batch runs from the job definition are suspended. From the action menu of a job definition, click **Disable definition**. After you select **Disable definition**, the menu item becomes **Enable definition**. Click **Enable definition** to re-enable batch runs from this job description. |
| Clone definition | Creates a new job definition populated with the values from an existing job definition. From the action menu of the existing job definition, click **Clone definition**, update the fields as needed, and click **Save prediction job definition**. Note that the **Jobs schedule** settings are turned off by default.|
| Delete definition | Deletes the job definition. Click **Delete definition**, and in the confirmation window, click **Delete definition** again. All scheduled jobs are cancelled.  |
---
title: Version 7.1.x maintenance releases
description: Maintenance releases for the DataRobot Release 7.1 major release.
---


# Version 7.1.x  maintenance releases {: #version-71x-maintenance-releases }

The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platform. See also the [features introduced in v7.1.0](v7.1/index), which was introduced _June 14, 2021_.


Version |	Release date
------- | -----------
[v7.1.4](v7.1.4-aml) | _December 21, 2021_
[v7.1.3](v7.1.3-aml) | _August 27, 2021_
[v7.1.2](v7.1.2-aml) | _August 2, 2021_
[v7.1.1](v7.1.1-aml) | _June 19, 2021_
# Version 7.3.4 {: #version-734 }

_February 7, 2022_

The DataRobot v7.3.4 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:

* [Features introduced in v7.3.0](v7.3.0-aml)

## Issues fixed in v7.3.4 {: #issues-fixed-in-v734 }

The following issues have been fixed since Enterprise release v7.3.3:

### Enterprise {: #enterprise }

* EP-1965: Updates Patroni to use md5 as password encryption for backward compatibility.

### Platform {: #platform }

* PLT-5899: Fixes an LDAP group mapping issue that prevented users from signing in when `USER_AUTH_LDAP_SEARCH_BASE_DN` was configured.

### Predictions {: #predictions }

* PRED-6605: Fixes an issue where some error messages would not display when using batch predictions and GCP.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: AWS
description: Integrate DataRobot with Amazon Web Services.

---

# AWS {: #aws }

The sections described below provide techniques for integrating Amazon Web Services with DataRobot.

Topic | Describes...
----- | ------
[Import data from AWS S3](import-from-aws-s3) | Importing data from AWS S3 to AI Catalog and creating an ML project.
[Deploy models on EKS](deploy-dr-models-on-aws) | Deploying and monitor DataRobot models on AWS Elastic Kubernetes Service (EKS) clusters.
[Path-based routing to PPS on AWS](path-based-routing-to-pps-on-aws) | Using a single IP address for all Portable Prediction Servers through path-based routing.
[Score Snowflake data on AWS EMR Spark](score-snowflake-aws-emr-spark) | Scoring Snowflake data via DataRobot models on AWS Elastic Map Reduce (EMR) Spark.
[Ingest data with AWS Athena](ingest-athena) | Ingesting AWS Athena and Parquet data for machine learning.

## Lambda {: #lambda }

Topic | Describes...
----- | ------
[Serverless MLOps agents](monitor-serverless-mlops-agents) | Monitoring external models with serverless MLOps agents running on AWS Lambda.
[AWS Lambda reporting to MLOps](aws-lambda-reporting-to-mlops) | AWS Lambda serverless reporting of actuals to DataRobot MLOps.
[Use DataRobot Prime models with AWS Lambda](prime-lambda) | Using DataRobot Prime models with AWS Lambda.
[Use Scoring Code with AWS Lambda](sc-lambda) | Making predictions using Scoring Code deployed on AWS Lambda.

## SageMaker {: #sagemaker }

Topic | Describes...
----- | ------
[Deploy models on Sagemaker](sagemaker-deploy) | Deploying on SageMaker and monitoring with MLOps agents.
[Monitor SageMaker models in MLOps](sagemaker-monitor) | Monitoring a SageMaker model that has been deployed to AWS for real-time scoring in DataRobot MLOps.
[Use Scoring Code with AWS SageMaker](sc-sagemaker) | Making predictions using Scoring Code deployed on AWS SageMaker.
---
title: Blueprint editor detailed view
description: Toggle between a summary view and a detailed view when working with the Composable ML blueprint editor.
section_name: AutoML
maturity: public-preview
---

# Blueprint editor detailed view {: #blueprint-editor-detailed-view }

!!! info "Availability information"
    Blueprint view toggle is off by default. Contact your DataRobot representative or administrator for information on enabling the feature.

    <b>Feature flag:</b> Enable Blueprint Detailed View Toggle
    
When DataRobot builds models, it automatically selects the best predictive models for the specified target feature. These models derive from blueprints (modeling algorithms), which are composed of tasks. Initially, the blueprint includes all the tasks that are part of the algorithm, but in the process of creating the final blueprint, DataRobot "prunes" unnecessary branches. 

## From the Leaderboard {: #from-the-leaderboard }

The blueprint shown from the Leaderboard's [**Blueprint**](blueprints) tab is a view-only representation of the model. It reflects the tasks used to train the model&mdash;a _summary view_. 

![](images/blue-toggle-1.png)

You can, however, show the full blueprint. To enable a _detailed view_ that displays all the branches of the original algorithm, click the **Show full blueprint** toggle:

![](images/blue-toggle-2.png)

If a model uses all of the feature types contained in the project data (numeric, categorical, date, text etc.), the full blueprint toggle is disabled. This is because the summary and detailed blueprints will be the same (all tasks were used). 



## From the blueprint editor {: #from-the-blueprint-editor }

When you click **Copy and Edit**, DataRobot opens the [blueprint editor](cml-blueprint-edit#access-the-blueprint-editor). The editor opens in the _detailed view_ of the blueprint. In this case, the toggle is disabled because the version used for modeling is not relevant to editing the complete blueprint.

![](images/blue-toggle-3.png)
Select the settings wheel to configure notebook settings.

{% if section(page, "dr-notebooks") %}

![](images/nb-4.png)

{% else %}

![](images/wb-nb-4.png)

{% endif %}

These settings allow you to control the display of:

 * Line numbers in code blocks.
 * Cell titles and outputs.
 * Cell output scrolling.

Select the actions menu ![](images/icon-menu.png) to access additional notebook actions:

 * Download the notebook as an `.ipynb` file.
 * Duplicate the notebook.
 * Delete the notebook. Note that deleting a notebook will also delete its associated assets, such as the notebook's environment variables and any revision history.

### Notebook metadata {: #notebook-metadata }

Click the info icon ![](images/icon-info-circle.png) to edit the notebook's metadata:

{% if section(page, "dr-notebooks") %}

![](images/nb-3.png)

{% else %}

![](images/wb-nb-3.png)

{% endif %}

* **Tags**: (Optional) Enter one or more descriptive tags that you can use to filter notebooks when viewing them in the dashboard.

* **Description**: (Optional) Enter a description of the notebook.